Searching and Learning by Trial and Error
I study a dynamic model of trial-and-error search in which agents do not have complete knowledge of how choices are mapped into outcomes. Agents learn about the mapping by observing the choices of earlier agents and the outcomes that are realized. The key novelty is that the mapping is represented as the realized path of a Brownian motion. I characterize for this environment the optimal behavior each period as well as the trajectory of experimentation and learning through time. Applied to new product development, the model shares features of the data with the well-known Product Life Cycle. (JEL D81, D83, D92, L26)
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Volume (Year): 101 (2011)
Issue (Month): 6 (October)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Aghion Philippe & Bolton, Patrick & Harris Christopher & Jullien Bruno, 1991.
"Optimal learning by experimentation,"
CEPREMAP Working Papers (Couverture Orange)
- Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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- Robert S. Gibbons, 2010.
"Inside Organizations: Pricing, Politics, and Path Dependence,"
Levine's Working Paper Archive
661465000000000249, David K. Levine.
- Robert Gibbons, 2010. "Inside Organizations: Pricing, Politics, and Path Dependence," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 337-365, 09.
- Daniel A. Levinthal, 1997. "Adaptation on Rugged Landscapes," Management Science, INFORMS, vol. 43(7), pages 934-950, July.
- repec:oup:restud:v:58:y:1991:i:4:p:621-54 is not listed on IDEAS
- Stefan H. Thomke, 1998. "Managing Experimentation in the Design of New Products," Management Science, INFORMS, vol. 44(6), pages 743-762, June.
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